Chapter 9: Semi- and Nonparametric Forecasting
Data Sets
Chapter-9-data.zip
Duration-waiting-stand-biv-3col.dat
EXPAR2.dat
Flow.dat
Flow394.dat
Geyser_waiting.dat
GSL.dat
GSL519.dat
Rain.dat
Rain394.dat
SSTdat
SSTgranite.dat
Yt-n500-sinus.dat
Chapter-9-data.zip
Duration-waiting-stand-biv-3col.dat
EXPAR2.dat
Flow.dat
Flow394.dat
Geyser_waiting.dat
GSL.dat
GSL519.dat
Rain.dat
Rain394.dat
SSTdat
SSTgranite.dat
Yt-n500-sinus.dat
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Computer Codes
Examples: Example_9-8.zip Example_9-9.zip Example_9-10.zip Exercises: Exercise_9-1.zip Exercise_9-2a-b.zip Exercise_9-2b.zip Exercise_9-4.zip Exercise_9-5.zip Miscellanea: Algorithm-93.ox FCAR.zip FPE-additive.zip Mean_median.m Figures Figures-Chapter-9-exercises.zip Figures-Chapter-9-exercises-jpg.zip |
(R code) (S-Plus) (SAS code) (M code) (M code) (R code) (M code) (R code) (Ox code) (S-Plus) (G code) (M code) (EPS format) (JPEG format) |
Links to Websites with Supplementary Material
- Click on the following link for getting access to Ron Gallant's webpage with C code of SNP: A program for nonparametric time series analysis. Reference: Gallant, A.R. and Tauchen, G. (1992), A nonparametric Approach to Nonlinear Time Series Analysis: Estimation and Simulation. In D.R. Brillinger et al. (Eds.) New Directions in Time Series Analysis, Part II. Spinger-Verlag, New York, pp. 71-92. Abstract: http://www.aronaldg.org/pubs/ima92.html.
- Click on the following link for getting access to data sets, figures, and computer codes to accompany Fan and Yao (2003), Nonlinear Time Series: Nonparametric and Parametric Methods (Springer-Verlag, New York) .
- Click on the following link for getting access to Gints Jekabsons' webpage with the Adaptive Regression Splines (ARESLab) MATLAB/Octave toolbox.
- Rafael A. Irizarry (2001, The American Statistician) presents a more general version of local (nonparametric) regression (e.g. Loess), called local harmonic regression or lohess. The S-Plus scripts used in the paper are available at: www.biostat.jhsph.edu/~ririzarr/software.html.
- Click on the following link for downloading the kernel smoothing MATLAB toolbox, to accompany Horová, Koláček and Zelinka, Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. World Scientific Publishing Co. Pte. Ltd., 2012.
- Click on the following link for downloading MATLAB and R codes, to accompany the paper by Chen and Huo (2009, J. Computational and Statistical Graphics).
